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- W4294975676 abstract "Automatic surgical phase recognition plays a key role in surgical workflow analysis and overall optimization in clinical work. In the complicated surgical procedures, similar inter-class appearance and drastic variability in phase duration make this still a challenging task. In this paper, a spatio-temporal transformer is proposed for online surgical phase recognition with different granularity. To extract rich spatial information, a spatial transformer is used to model global spatial dependencies of each time index. To overcome the variability in phase duration, a temporal transformer captures the multi-scale temporal context of different time indexes with a dual pyramid pattern. Our method is thoroughly validated on the public Cholec80 dataset with 7 coarse-grained phases and the CATARACTS2020 dataset with 19 fine-grained phases, outperforming state-of-the-art approaches with 91.4% and 84.2% accuracy, taking only 24.5M parameters." @default.
- W4294975676 created "2022-09-08" @default.
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- W4294975676 date "2022-07-11" @default.
- W4294975676 modified "2023-09-29" @default.
- W4294975676 title "Spatio-Temporal Causal Transformer for Multi-Grained Surgical Phase Recognition" @default.
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- W4294975676 doi "https://doi.org/10.1109/embc48229.2022.9871004" @default.
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